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authorAlexander Kabui2022-03-18 13:49:19 +0300
committerGitHub2022-03-18 13:49:19 +0300
commit205ebb2d9501f431984359b15467cf573803b0a4 (patch)
treee1882e773f89294d15c8d3cc139fa0234838bc2a /wqflask
parent13d00e885600157cc253e9d03f26e712fed17346 (diff)
parent6359dc2bf8973991072634e6a2b8d6a8a038166a (diff)
downloadgenenetwork2-205ebb2d9501f431984359b15467cf573803b0a4.tar.gz
Merge pull request #671 from Alexanderlacuna/feature/gn3-pca
Replace pca rpy2 code
Diffstat (limited to 'wqflask')
-rw-r--r--wqflask/wqflask/correlation_matrix/show_corr_matrix.py160
1 files changed, 38 insertions, 122 deletions
diff --git a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
index e7b16e77..88d62045 100644
--- a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
+++ b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
@@ -19,27 +19,25 @@
# This module is used by GeneNetwork project (www.genenetwork.org)
import datetime
-import math
import random
import string
-
-import rpy2.robjects as ro
-from rpy2.robjects.packages import importr
-
import numpy as np
import scipy
-from base import data_set
+from base.data_set import create_dataset
from base.webqtlConfig import GENERATED_TEXT_DIR
-from functools import reduce
-from functools import cmp_to_key
-from utility import webqtlUtil
-from utility import helper_functions
-from utility import corr_result_helpers
+
+
+from utility.helper_functions import get_trait_db_obs
+from utility.corr_result_helpers import normalize_values
from utility.redis_tools import get_redis_conn
-Redis = get_redis_conn()
-THIRTY_DAYS = 60 * 60 * 24 * 30
+
+from gn3.computations.pca import compute_pca
+from gn3.computations.pca import process_factor_loadings_tdata
+from gn3.computations.pca import generate_pca_temp_traits
+from gn3.computations.pca import cache_pca_dataset
+
class CorrelationMatrix:
@@ -47,11 +45,10 @@ class CorrelationMatrix:
trait_db_list = [trait.strip()
for trait in start_vars['trait_list'].split(',')]
- helper_functions.get_trait_db_obs(self, trait_db_list)
+ get_trait_db_obs(self, trait_db_list)
self.all_sample_list = []
self.traits = []
- self.insufficient_shared_samples = False
self.do_PCA = True
# ZS: Getting initial group name before verifying all traits are in the same group in the following loop
this_group = self.trait_list[0][1].group.name
@@ -116,7 +113,7 @@ class CorrelationMatrix:
if sample in self.shared_samples_list:
self.shared_samples_list.remove(sample)
- this_trait_vals, target_vals, num_overlap = corr_result_helpers.normalize_values(
+ this_trait_vals, target_vals, num_overlap = normalize_values(
this_trait_vals, target_vals)
if num_overlap < self.lowest_overlap:
@@ -165,16 +162,13 @@ class CorrelationMatrix:
self.pca_works = "False"
try:
- corr_result_eigen = np.linalg.eig(np.array(self.pca_corr_results))
- corr_eigen_value, corr_eigen_vectors = sortEigenVectors(
- corr_result_eigen)
- if self.do_PCA == True:
+ if self.do_PCA:
self.pca_works = "True"
self.pca_trait_ids = []
- pca = self.calculate_pca(
- list(range(len(self.traits))), corr_eigen_value, corr_eigen_vectors)
- self.loadings_array = self.process_loadings()
+ pca = self.calculate_pca()
+ self.loadings_array = process_factor_loadings_tdata(
+ factor_loadings=self.loadings, traits_num=len(self.trait_list))
else:
self.pca_works = "False"
except:
@@ -187,66 +181,31 @@ class CorrelationMatrix:
samples=self.all_sample_list,
sample_data=self.sample_data,)
- def calculate_pca(self, cols, corr_eigen_value, corr_eigen_vectors):
- base = importr('base')
- stats = importr('stats')
-
- corr_results_to_list = ro.FloatVector(
- [item for sublist in self.pca_corr_results for item in sublist])
-
- m = ro.r.matrix(corr_results_to_list, nrow=len(cols))
- eigen = base.eigen(m)
- pca = stats.princomp(m, cor="TRUE")
- self.loadings = pca.rx('loadings')
- self.scores = pca.rx('scores')
- self.scale = pca.rx('scale')
+ def calculate_pca(self):
- trait_array = zScore(self.trait_data_array)
- trait_array_vectors = np.dot(corr_eigen_vectors, trait_array)
+ pca = compute_pca(self.pca_corr_results)
- pca_traits = []
- for i, vector in enumerate(trait_array_vectors):
- # ZS: Check if below check is necessary
- # if corr_eigen_value[i-1] > 100.0/len(self.trait_list):
- pca_traits.append((vector * -1.0).tolist())
+ self.loadings = pca["components"]
+ self.scores = pca["scores"]
this_group_name = self.trait_list[0][1].group.name
- temp_dataset = data_set.create_dataset(
- dataset_name="Temp", dataset_type="Temp", group_name=this_group_name)
+ temp_dataset = create_dataset(
+ dataset_name="Temp", dataset_type="Temp",
+ group_name=this_group_name)
temp_dataset.group.get_samplelist()
- for i, pca_trait in enumerate(pca_traits):
- trait_id = "PCA" + str(i + 1) + "_" + temp_dataset.group.species + "_" + \
- this_group_name + "_" + datetime.datetime.now().strftime("%m%d%H%M%S")
- this_vals_string = ""
- position = 0
- for sample in temp_dataset.group.all_samples_ordered():
- if sample in self.shared_samples_list:
- this_vals_string += str(pca_trait[position])
- this_vals_string += " "
- position += 1
- else:
- this_vals_string += "x "
- this_vals_string = this_vals_string[:-1]
- Redis.set(trait_id, this_vals_string, ex=THIRTY_DAYS)
- self.pca_trait_ids.append(trait_id)
+ pca_temp_traits = generate_pca_temp_traits(species=temp_dataset.group.species, group=this_group_name,
+ traits_data=self.trait_data_array, corr_array=self.pca_corr_results,
+ dataset_samples=temp_dataset.group.all_samples_ordered(),
+ shared_samples=self.shared_samples_list,
+ create_time=datetime.datetime.now().strftime("%m%d%H%M%S"))
- return pca
+ cache_pca_dataset(redis_conn=get_redis_conn(
+ ), exp_days=60 * 60 * 24 * 30, pca_trait_dict=pca_temp_traits)
- def process_loadings(self):
- loadings_array = []
- loadings_row = []
- for i in range(len(self.trait_list)):
- loadings_row = []
- if len(self.trait_list) > 2:
- the_range = 3
- else:
- the_range = 2
- for j in range(the_range):
- position = i + len(self.trait_list) * j
- loadings_row.append(self.loadings[0][position])
- loadings_array.append(loadings_row)
- return loadings_array
+ self.pca_trait_ids = list(pca_temp_traits.keys())
+
+ return pca
def export_corr_matrix(corr_results):
@@ -261,11 +220,11 @@ def export_corr_matrix(corr_results):
output_file.write("\n")
output_file.write("Correlation ")
for i, item in enumerate(corr_results[0]):
- output_file.write("Trait" + str(i + 1) + ": " + \
+ output_file.write("Trait" + str(i + 1) + ": " +
str(item[0].dataset.name) + "::" + str(item[0].name) + "\t")
output_file.write("\n")
for i, row in enumerate(corr_results):
- output_file.write("Trait" + str(i + 1) + ": " + \
+ output_file.write("Trait" + str(i + 1) + ": " +
str(row[0][0].dataset.name) + "::" + str(row[0][0].name) + "\t")
for item in row:
output_file.write(str(item[1]) + "\t")
@@ -275,57 +234,14 @@ def export_corr_matrix(corr_results):
output_file.write("\n")
output_file.write("N ")
for i, item in enumerate(corr_results[0]):
- output_file.write("Trait" + str(i) + ": " + \
+ output_file.write("Trait" + str(i) + ": " +
str(item[0].dataset.name) + "::" + str(item[0].name) + "\t")
output_file.write("\n")
for i, row in enumerate(corr_results):
- output_file.write("Trait" + str(i) + ": " + \
+ output_file.write("Trait" + str(i) + ": " +
str(row[0][0].dataset.name) + "::" + str(row[0][0].name) + "\t")
for item in row:
output_file.write(str(item[2]) + "\t")
output_file.write("\n")
return corr_matrix_filename, matrix_export_path
-
-
-def zScore(trait_data_array):
- NN = len(trait_data_array[0])
- if NN < 10:
- return trait_data_array
- else:
- i = 0
- for data in trait_data_array:
- N = len(data)
- S = reduce(lambda x, y: x + y, data, 0.)
- SS = reduce(lambda x, y: x + y * y, data, 0.)
- mean = S / N
- var = SS - S * S / N
- stdev = math.sqrt(var / (N - 1))
- if stdev == 0:
- stdev = 1e-100
- data2 = [(x - mean) / stdev for x in data]
- trait_data_array[i] = data2
- i += 1
- return trait_data_array
-
-
-def sortEigenVectors(vector):
- try:
- eigenValues = vector[0].tolist()
- eigenVectors = vector[1].T.tolist()
- combines = []
- i = 0
- for item in eigenValues:
- combines.append([eigenValues[i], eigenVectors[i]])
- i += 1
- sorted(combines, key=cmp_to_key(webqtlUtil.cmpEigenValue))
- A = []
- B = []
- for item in combines:
- A.append(item[0])
- B.append(item[1])
- sum = reduce(lambda x, y: x + y, A, 0.0)
- A = [x * 100.0 / sum for x in A]
- return [A, B]
- except:
- return []